Improving Radiologist Detection of Lung Nodules with CAD

描述(由申请人提供)： 肺癌是美国癌症相关死亡的主要原因。原发癌和转移性肺癌最常见的表现为肺结节，这很容易在放射学上显示出来。CT扫描是目前可用于检测肺结节的最敏感的非侵入性手段，但灵敏度有限，观察者之间的变异性很高，尤其是较小的结节。最近发展起来的多层螺旋CT(MDCT)可以在不到10秒的屏气时间内，以前所未有的三维空间分辨率对肺部进行成像，其分辨率比单排CT系统大10倍。放射科医生要利用MDCT数据更高的空间分辨率来提高肺结节的检测，必须克服两个关键挑战-(1)对高分辨率MDCT扫描产生的300-600幅图像进行时间效率的解释，以及(2)在检查1 mm厚的CT切片时提高结节检测的敏感度，同时不失去特异性，因为与厚层扫描相比，厚层CT切片的外观更相似，因此更难区分肺结节和血管。因此，这项提议的重点是开发一种利用CT检测肺癌的优化方法。我们的具体目标是： 1.开发一种从肺部CT数据中自动检测肺结节的技术。 2.在放射科医生对CT图像进行初步评估后，考虑计算机辅助检测(CAD)的结果，以确定放射科医生在检测疑似患有肺结节的患者方面的敏感度和观察者之间的一致性的改善。我们将通过对从美国不同地区的两个医疗中心获得的肺结节的CT扫描进行培训来优化我们的CAD系统，我们将展示CAD可以像第二位放射科医生一样有效地提高放射科医生在CT扫描中检测肺结节的能力，而不会大幅增加假阳性检测。这项工作完成后，我们将使放射科医生能够更好地利用MDCT扫描仪提供的更好的数据，大大提高他们在CT扫描中发现肺结节的能力，从而为改善肺癌患者的管理和护理做出贡献。

DESCRIPTION (provided by applicant): Lung cancer is the leading cause of cancer-related deaths in the United States. Both primary and metastatic lung cancer most commonly manifest as pulmonary nodules, which are readily visualized radiographically. CT scanning is currently the most sensitive non-invasive means available for detecting pulmonary nodules, but has suffered from limited sensitivity and high interobserver variability, particularly of smaller nodules. The recent development of multi-detector-row CT (MDCT) allows imaging of the lungs with unprecedented three-dimensional spatial resolution, up to 10 times greater than single-row CT systems within a single less than 10 second breathhold. For radiologists to harness the higher spatial resolution of MDCT data to improve lung nodule detection, they must overcome two key challenges - (1) time efficient interpretation of the 300-600 images that result from high-resolution MDCT scans of the lungs and (2) improve nodule detection sensitivity without losing specificity when examining 1-mm thick CT sections, where lung nodule and blood vessel discrimination is more difficult due to the greater similarity of their appearance when compared to thick-section acquisitions. The focus of this proposal, therefore, is to develop an optimized approach toward the detection of lung cancer with CT. Our specific aims are: 1. To develop an automatic technique for detecting pulmonary nodules from lung CT data. 2. To determine the improvement in radiologist sensitivity and interobserver agreement for the detection of pulmonary nodules in patients suspected of having them when computer-aided detection (CAD) results are considered following initial radiologist assessment of CT images. We will optimize our CAD system by training on CT scans of pulmonary nodules obtained from two medical centers in different regions of the United States, and we will show that CAD can be as effective as a second radiologist in improving a radiologist's ability to detect pulmonary nodules on CT scans without substantially increasing falsely positive detections. Upon completion of this work, we will have enabled radiologists to take better advantage of the improved data available from MDCT scanners and substantially improve their ability to detect pulmonary nodules on CT scans, and thereby contribute to improvements in the management and care of patients with lung cancer.